LangChain and LangGraph come from the same team and are often mentioned in the same breath, which leads a lot of people to think they have to choose one over the other. In reality they solve different problems and frequently work together. LangChain is the broad framework for building LLM applications, while LangGraph is a focused library for building complex, stateful agents and workflows. Knowing which to reach for, and when to use both, saves real confusion. This guide compares LangChain and LangGraph in 2026, explains what each is best at, and helps you pick the right tool for what you are building.
If you are still getting your bearings with the broader framework first, our explainer on what LangChain is is a useful starting point. Here we focus on how the two compare.
Quick verdict
Use LangChain for straightforward LLM applications, retrieval-augmented question answering, chatbots, and linear multi-step flows, where its components and integrations save you the most work. Use LangGraph when you need complex, stateful agents that loop, branch, and make decisions in ways a simple chain cannot express. They are complementary, not rivals, and many real applications use both.
LangChain vs LangGraph at a glance
| LangChain | LangGraph | |
|---|---|---|
| What it is | Broad LLM app framework | Library for stateful agents and workflows |
| Model of execution | Chains, mostly linear flows | Graphs that loop and branch |
| Best for | RAG, chatbots, simple pipelines | Complex, controllable agents |
| State | Lighter | First-class, explicit state |
| Relationship | The foundation and components | Built to work alongside LangChain |
What LangChain is best at
LangChain is the broad framework that gives you the building blocks of an LLM application: a consistent interface to models, prompt management, memory, document loading and retrieval, and tools, along with chains that link steps into a flow. Its strength is breadth and a vast library of integrations, which makes it the fastest way to assemble the common patterns.
It is the right choice when your application is, at its heart, a sequence of steps. A retrieval-augmented question answering system, take a question, retrieve relevant chunks, send them to the model, return an answer, is a natural fit, as are chatbots with memory and pipelines that process or extract from documents. For these, LangChain’s components do most of the work and you write very little plumbing. The execution is largely linear: one step feeds the next, and that is exactly what a great many LLM applications need. If your flow does not require the model to loop back, branch based on intermediate results, or maintain elaborate state across many steps, LangChain is all you need and the simpler choice.
What LangGraph is best at
LangGraph exists for the cases where a linear chain is not enough. It lets you build your application as a graph, where nodes are steps and the connections between them can loop, branch, and route based on the current state. This is the model you need for sophisticated agents: an agent that tries something, checks the result, decides whether to try again or take a different path, and keeps working toward a goal across many steps is exactly what graphs express well and chains express poorly.
The other thing LangGraph treats as first-class is state. In a complex agent, you need to track what has happened, what the agent knows, and where it is in its task, and have that state drive decisions. LangGraph makes that explicit and controllable, which is essential for reliability when an agent is doing something involved. It also supports patterns like human-in-the-loop, where execution can pause for a person to approve or correct a step before continuing, which matters for agents that take real actions. In short, when you need control, loops, branching, and durable state, an agent that genuinely reasons through a multi-step task rather than running a fixed pipeline, LangGraph is the tool built for it.
How they work together
The most important thing to understand is that this is rarely an either-or decision. LangGraph is designed to work alongside LangChain, not to replace it. You commonly use LangChain’s components, its model interfaces, retrievers, and tools, as the building blocks inside a LangGraph graph. LangChain provides the pieces, and LangGraph provides the control flow that orchestrates them into a sophisticated agent.
So a realistic application might use LangChain for its retrieval and model components, and LangGraph to coordinate an agent that decides when to retrieve, when to call a tool, when to ask the user, and when it is done. Thinking of them as competitors misses the point: LangChain is the toolkit, and LangGraph is the way to wire those tools into complex, stateful behavior. For simple flows you may only need LangChain, and for complex agents you reach for LangGraph on top of it.
Which should you choose?
The decision comes down to the complexity of what you are building.
Choose LangChain alone when your application is a linear or lightly branching flow: retrieval-augmented question answering, a chatbot with memory, a document processing pipeline, or any case where the steps run in sequence. This covers a large share of real LLM applications, and adding graph machinery you do not need only adds complexity.
Choose LangGraph (with LangChain) when you are building a genuine agent that must loop, branch, maintain state, and make decisions across many steps, or when you need human-in-the-loop control and reliability in a complex workflow. If you find yourself fighting to express your logic as a simple chain, that is the signal you have outgrown it and should move to a graph.
Start simple and grow. A sensible path is to begin with LangChain, and adopt LangGraph if and when your application’s complexity demands it. Many projects never need more than LangChain, and those that do can introduce LangGraph for the agentic parts without throwing away their LangChain components. For more on the agent landscape, see our guide to the best AI agents.
The shared foundation: retrieval and a vector database
Whether you build with LangChain alone or add LangGraph, if your application uses your own data it relies on retrieval, and that means a vector database underneath. Both approaches use the same retrieval components and the same kind of database to store embeddings and find the relevant pieces of your content at query time. The choice of LangChain versus LangGraph is about orchestration and control flow, while the vector database is the storage and search layer beneath, and you need it either way.
For that layer, a managed vector database keeps things simple, which is why Pinecone is a common choice with both LangChain and LangGraph: it scales the retrieval layer without you running infrastructure and integrates with minimal code. You focus on your application logic, simple chain or complex agent, and let Pinecone handle storage and similarity search. For the full set of options, including self-hosted ones, see our guide to the best vector database for RAG.
Build your retrieval layer on Pinecone
Whether you use LangChain or LangGraph, Pinecone gives you a managed, serverless vector database that plugs in with minimal code and scales the retrieval layer for you. The simple foundation for RAG and agents.
Frequently asked questions
What is the difference between LangChain and LangGraph? LangChain is a broad framework that provides the building blocks of LLM applications, models, prompts, memory, retrieval, and tools, with chains for mostly linear flows. LangGraph is a focused library for building complex, stateful agents and workflows as graphs that can loop, branch, and maintain explicit state. LangChain is the toolkit; LangGraph is the way to orchestrate it into sophisticated agents.
Do I have to choose between LangChain and LangGraph? No. They are complementary and from the same team. You commonly use LangChain’s components inside a LangGraph graph, with LangChain providing the pieces and LangGraph providing the control flow. For simple flows you may only need LangChain; for complex agents you use LangGraph on top of it.
When should I use LangGraph instead of LangChain? Use LangGraph when your application needs to loop, branch, maintain state across many steps, or support human-in-the-loop control, which is the case for genuine agents that reason through a multi-step task. If your flow runs in sequence, like RAG or a chatbot, LangChain alone is simpler and enough.
Is LangGraph replacing LangChain? No. LangGraph is built to work with LangChain, not replace it. LangChain remains the framework of components and integrations, and LangGraph adds a powerful way to orchestrate them into stateful, controllable agents. Many applications use both together.
Which is better for a beginner? LangChain, in most cases. It covers the common patterns, RAG, chatbots, pipelines, with less conceptual overhead. Start with LangChain, and adopt LangGraph later if your application grows into something that needs complex agentic control.
Do both use a vector database? Yes, if your application retrieves over your own data. Retrieval relies on a vector database to store embeddings and find relevant content, regardless of whether you orchestrate with LangChain or LangGraph. A managed option like Pinecone works with both.
The bottom line
LangChain and LangGraph are not competitors but two parts of the same toolkit, and the right choice depends on the complexity of what you are building. Use LangChain on its own for the many applications that are linear or lightly branching, retrieval-augmented question answering, chatbots, and document pipelines, where its components do most of the work. Reach for LangGraph, layered on top of LangChain, when you need a genuine agent that loops, branches, maintains state, and makes decisions across many steps. Start simple and add the graph machinery only when your application demands it. Either way, if you retrieve over your own data, a managed vector database like Pinecone is the foundation underneath. For more, see our explainer on what LangChain is and our guide to the best vector database for RAG.
